Model-free tuning strategy of fractional-order PI controller for speed regulation of permanent magnet synchronous motor

Abstract

This paper investigates a model-free tuning method of a fractional-order proportional–integral (FOPI) controller and its application for the speed regulation of a permanent magnet synchronous motor (PMSM). Firstly, the presented practical FOPI tuning method formulates the FOPI controller parameter identification problem via virtual reference feedback tuning (VRFT). Under the lack of accurate models, the proposed model-free method depends only on the measured input–output data of the closed-loop PMSM servo system. Secondly, Bode’s ideal transfer function is incorporated into the VRFT with consideration of the systematic fractional dynamics. Thus, the properties of the resulting system may be approximated to the desired fractional-order reference model. Thirdly, the proposed method fully considers optimal performance constraints on the stability requirements, sensitivity criteria, frequency-domain and time-domain characteristics. Then, the comprehensive optimization problem is derived and solved. Using suitably tuned parameters, the robustness and disturbance rejection ability of the VRFT-based FOPI control system are enhanced to achieve optimal performance. The convergence of the proposed method is proved by theoretical analysis. Finally, experimental results are presented to illustrate the effectiveness of the proposed model-free FOPI control method for the PMSM servo system.

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“…However, obtaining a reliable model of a system from the first principle or using system identification tools, is a quite difficult and time-consuming task, especially for modern complex systems. For this reason, several techniques were developed for model-free or data-driven control design including model-free adaptive control (MFAC) [24], virtual reference feedback tuning (VRFT) [25], and iterative feedback tuning (IFT) [26]. In VRFT and IFT techniques, the controller structure is identified first and the input-output data are used to tune the control parameters offline.…”

An improved nonlinear proportional-integral-differential controller combined with fractional operator and symbolic adaptation algorithm

“…The experiments showed that fractional order controllers provided superior control characteristics compared to conventional PID. Xie et al (2018) investigated a model-free tuning method of fractional order PI controller. The controller parameter identification method depends only on the measured input-output data of the closed-loop PMSM servo system.…”

A fractional super-twisting control of electrically driven mechanical systems

“…Electromechanical systems are prone to a broad class of disturbances and dynamic uncertainties, (Das, 2018; Kim, et al 2018; Sun, et al 2018; Xie et al, 2019; Xu et al, 2017), which in most cases are neglected depending on the operational regime. However, for precision tasks some non-smooth effects may disrupt the system performance, if they are not actively compensated through the dynamic controller, for instance, backlash (Ahangarian-Abhari et al, 2018; Li and Yang, 2019; Tenreiro-Machado, 2013), dynamic friction effects (Huang and Chiu, 2009), and not necessarily integer-order differentiable disturbances due to input noise and mechanical vibrations (Loutridis and Trochidis, 2004).…”